This paper addresses the challenge of handling extreme class imbalance, defined here as a situation in which negative examples, conventionally the majority, outnumber positive examples, usually the ones of most interest, by a factor of 500 or more (in other words, have an imbalance ratio (IR)\(\ge \)  500). Such problems are not in fact uncommon, and arise in application areas such as fraud detection [1]} and cheminformatics [2]}. We make use of two methods, that tackle different, but complementary, aspects of the class imbalance problem:
